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This paper explores the role of accounting and accountability techniques in contributing to Australia’s border industrial complex.

We use the political thought of Behrouz Boochani to explore the role that accounting techniques play at the micro and macro level of his dialectic of alienation and freedom. Firstly, we explore the accounting and accountability techniques detailed in Boochani’s No Friend but the Mountain, which gives an account of his life in Manus Prison, and the accounting techniques he experienced. Secondly, we explore the discourse of alienation created within the annual reporting of the Australian Federal Government regarding the border industrial complex.

We argue that the border industrial complex requires the alienation of asylum seekers from their own humanity for capital accumulation, and that accounting and accountability techniques facilitate this form of alienation. These techniques include inventorying, logging and queuing at the micro level within Manus Prison. This alienates those trapped in the system from one another and themselves. Techniques also include annual reporting at a macro level which alienates those trapped in the system from the (White) “Australian Community”. However, these techniques are resisted at every point by assertions of freedom.

We illustrate the role of accounting in accumulation by alienation, where the unfreedom of incarcerated asylum seekers is a site of profit for vested interests. But also that this alienation is resisted at every point by refusals of alienation as assertions of freedom. Thus, this study contributes to the accounting literature by drawing from theories of alienation, and putting forward the dialectic of alienation and freedom articulated by Boochani and collaborators.

This paper aims to explore the use and impact of social media, specifically Twitter (now X), in political mobilization in Pakistan. It focuses on the events followed by the no-confidence motion against Imran Khan as Pakistan’s prime minister in April 2022 and the protest campaign that ensued, facilitated through the strategic use of the Urdu hashtag #امپورٹڈ_حکومت_نامنظور (translated as “imported-government unacceptable”) on Twitter, both within and outside Pakistan.

Using Web scraping, data from Twitter was extracted and analyzed between 2022 and 2023. By probing into user account profiles and interactions with this hashtag, this paper investigates the claims surrounding the hashtag’s popularity, by identifying suspicious accounts and their contributions in the trending of the hashtag.

Findings suggest that the claim of the hashtag's unprecedented success was overhyped, further suggesting that the popularity and impact of the social media campaign were exaggerated. Despite high engagement rates, the study indicates a discrepancy between perceived influence and actual impact on public sentiment and political mobilization.

This paper contributes to the literature on social media’s role in political mobilization and agenda-setting in the Pakistani context. More generally, understanding hashtag dynamics and their impact on shaping public opinion, may be beneficial to academics and practitioners in better understanding the role of digital platforms in the politics.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

This study aims to investigate the claim that there is no coherent and homogeneous body of concepts and practices that can be classified as “Islamic accounting”.

The study focuses specifically on Islamic accounting and uses a qualitative historical documentary analysis methodology to study an original manuscript from the 14th century.

The analysis of the manuscript argues that religious accounting can be seen as a value-based system for achieving social good and that in the context of Islamic accounting, it can be conceptualised as a coherent body of ideas and practices.

Firstly, the study conceptualises Islamic accounting as a homogeneous discipline with its own knowledge, concepts and practices. Secondly, it contributes to current accounting literature by examining an ancient manuscript from the 14th century, which serves as a foundation for understanding the Islamic accounting system within the context of accounting, religion and spirituality. The paper further contributes by arguing that this conceptualisation of religious accounting as a value-based approach enables its practitioners to evaluate their own accountabilities in delivering on socioeconomic objectives related to inter-human/environmental, social and financial transactions within the context of religious accounting practices.

Following the growing adoption of social media, many entrepreneurs are launching personal social media channels. This study focuses on the effect of entrepreneurs' shared information on We Media platforms on the value relevance of their earnings.

Using entrepreneurs' We Media data collected from A-share-listed companies on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2018, this study investigates the effect of the data on the value relevance of earnings using the modified Ohlson model. The authors applied textual analysis to retrieve entrepreneurial We Media data acquired manually from Weibo.

We Media platforms can increase the value relevance of earnings. Entrepreneurs can enhance investor trust by establishing social ties with investors. Investors are more likely to recognize earnings information publicized by enterprises, owing to internal consistency. Particularly, value relevance improves significantly with more personal information being posted and more “likes” being acquired on entrepreneurs' We Media accounts. This positive effect is more obvious in privately owned and highly marketized regions.

The findings extend the research on the economic consequences of We Media as an important information channel, enrich the research on the social media posting behavior of entrepreneurs and provide a reference for enterprises to instill trust using new information disclosure methods and for governments to establish a safe internet environment to promote the sustainable development of the capital market.

This study aims to assess the evolution of the value relevance of book value, earnings and its components in Mauritius, an African developing country, focusing on value relevance changes after International Financial Reporting Standards (IFRS) adoption and subsequent local reforms.

The study relies on a data set of 567 firm-year observations (2001–2018) and the Ohlson valuation model to investigate value relevance after IFRS adoption, the implementation of institutional reforms and enforcement reforms.

Firstly, the authors find support for a rise in the combined value relevance of earnings and book value, albeit that book value significantly contributes to changes over time. The findings highlight the combined importance of IFRS adoption with institutional and enforcement reforms to improve value relevance. Secondly, the authors do not find evidence of a shift in value relevance between earnings and book value. Third, the cash flow model reveals a higher level of significance relative to the earnings model.

The authors extend the value relevance literature in the context of African developing countries. The present findings underpin the need for a reinforcing of relevant institutional and enforcement frameworks to ensure the benefits of IFRS adoption materialise. The findings also offer a contribution of how developing countries’ experience IFRS post-adoption while adding to the dearth of studies analysing IFRS enforcement practices.

This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false positives, causing bank employees to spend unnecessary time manually checking transactions that do not constitute money laundering. Decreasing the number of false positives can free up resources for investigating money laundering.

This study uses unique bank data on small- and medium-sized enterprises (SMEs) to examine how various client risk classification models can predict future suspicious transactions. This study explores various sources of client risk data and machine-learning approaches.

Client risk classification models can accurately predict suspicious future transactions. Adding accounting data and credit score information to client risk classification dramatically improves accuracy. This makes it easier to balance the risk of missing suspicious transactions with the need to reduce the number of false positives.

The suggested approach with readily available data sources and a focus on classifying client risk in a dynamic model can help banks significantly improve their efficiency by targeting their AML efforts toward the riskiest clients.

To the best of the authors’ knowledge, this study is the first to empirically explore machine learning in client risk classification, document how machine learning in client risk classification can significantly reduce false positives by incorporating novel, but readily available sources, such as credit risk and accounting data.

This study examines the moderating role of debt sustainability on the nexus between financial and economic growth in African countries.

This study utilised data from various sources, such as the World Bank and International Monetary Fund databases, specifically the World Development Indicators and Financial Access Survey. The data covered the period from 2004 to 2021 and focused on 53 African countries to examine the moderating effect of debt sustainability on the relationship between financial inclusion and economic growth using a two-step generalised method of moments system with forward orthogonal deviations.

The study findings indicate a direct link between financial inclusion and economic growth in African nations. In particular, the availability and utilisation of mobile money services are significant factors in promoting financial inclusion. Our study also highlights that excessive debt can impede economic growth by limiting the capacity of financial institutions to offer loans and other vital financial services.

Policymakers in Africa should promote economic growth by prioritising financial inclusion through mobile money and ATMs while ensuring sustainable debt levels.

This study adds to the ongoing discussion on the relationship between FI and economic growth in African countries. It explores how debt sustainability affects this relationship, and emphasises the importance of finding a balance between financial inclusion and debt management for long-term economic growth and development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2024-0062

This study aims to investigate the motivations behind the issuance of financial instruments with characteristics of equity (FICE), economic consequences associated with their issuance and accounting classifications based on a value-relevance approach.

Using a sample of 169 financial and nonfinancial firms from 10 jurisdictions that adopted International Financial Reporting Standards, the authors use a difference-in-differences econometric approach.

The findings reveal that FICE issuers are more leveraged companies with higher costs of equity and, in some cases, lower effective tax rates. This evidence corroborates the hypothesis that issuers of FICEs seek to increase their book values of equity (accounting treatment as equity) and, simultaneously, generate deductible expenses for tax purposes (tax treatment as liability).

This finding suggests that market participants do not treat these instruments as regular equity but rather as quasi-equity. The findings suggest that a binary classification of FICE as debt or equity may not be the accounting treatment that best represents the underlying economic substance of these contracts. Furthermore, this study reinforces the IASB indication regarding to increase the FICE disclosure to allow stakeholders to better understand the economic essence of these bonds.

This study assesses the economic outcomes and market evaluation of a specific type of FICE that has not been previously studied, which is similar to the examples provided by the IASB in their materials on the subject.

This paper employs data from the World Bank to examine the effect of traditional banks on FinTech and financial development in the Southern African Development Community (SADC) region.

The study employs the Generalized Method of Moments (GMM) as the primary data analysis method.

The findings of the study demonstrate a bi-directional relationship between traditional financial institutions and FinTech. Traditional financial institutions are observed to facilitate the adoption of FinTech solutions, whilst the disruptive effects of FinTech incentivize traditional banks to adapt to the changing financial landscape and tailor their service and product offerings to reflect recent technological advancements. Consequently, there exists a positive relationship between traditional financial institutions and financial development in the SADC region.

Our findings suggest the need for market liberalization and enhanced institutional quality controls for policymakers. Traditional banks must adapt their business models and incorporate FinTech solutions to remain competitive and relevant. Collaborative partnerships between traditional banks and FinTech firms have emerged as a practical approach to leverage the strengths of both sectors.

This is one of the first studies to examine the role of traditional financial institutions in FinTech and financial development using GMM in the SADC region.